Improving discrimination in data envelopment analysis without losing information based on Renyi’s entropy
Improving discrimination in data envelopment analysis without losing information based on Renyi’s entropy
复制标题
基于 Renyi 熵，在不丢失信息的情况下改进数据包络分析的辨别力
DOI：
10.1007/s10100-018-0550-y
复制
发表时间：
2018
影响因子：
1.7
通讯作者：
Chao Liu
中科院分区：
文献类型：
--
作者：
Qiwei Xie;Yuanyuan Li;Lizheng Wang;Chao Liu
数据包络分析（DEA）在很大程度上取决于变量的维度，之前的研究通过以最小的信息损失降低维度来解决该问题。由于丢失的信息也会对评价性能产生影响，因此本文提出了一种在不丢失任何变量信息且不需要任何额外优惠信息的情况下提高DEA判别力的方法。此外，引入基于并行计算概念的加速方法来解决多子集问题。将所有可能的变量子集列为节点，然后计算每个节点下的DEA效率，并根据仁义熵科学地生成相应的信息纯度。随后，通过对信息纯度进行归一化得到节点的重要度，并通过重要度与对应节点下的效率的加权和最终生成综合效率得分。提供了两个具体示例来评估性能。
Data envelopment analysis (DEA) heavily depends on the dimensionality of the variables, and previous studies address the problem by decreasing the dimensionality with a minimal loss of information. Since the lost information can also have the impact on the evaluation performance, this paper accordingly proposes an approach to improve the discriminatory power of DEA without losing any variables information and without requiring any additional preferential information. Furthermore, an accelerating approach based on the concept of parallel computing is introduced to solve the multi-subsets problem. Listing all the possible variables subsets as the nodes, then the DEA efficiencies under each node are calculated, and the corresponding purity of information can be scientifically generated based on Renyi’s entropy. Subsequently, the important degrees of nodes are obtained by normalizing the purities of information, and the comprehensive efficiency scores can be finally generated by the weighted sum between the important degrees and the efficiencies under the corresponding node. Two specific examples are provided to evaluate the performance.